Why scaling requires different strategies in MLOps - Visual Breakdown

Process Flow - Why scaling requires different strategies

Start: Small Scale Model

↓

Evaluate Performance

↓

Need to Scale?

No→Continue Small Scale

Yes↓

Choose Scaling Strategy

↓

Vertical

↓

Adjust Resources

↓

Monitor & Optimize

↓

End

Shows decision steps from small scale to choosing and applying different scaling strategies.

Execution Sample

MLOps

if data_size < threshold:
    use_small_model()
else:
    if resource_limit:
        scale_vertically()
    else:
        scale_horizontally()

Decides scaling strategy based on data size and resource limits.

Process Table

Step	Condition Checked	Condition Result	Action Taken	System State
1	data_size < threshold	True	use_small_model()	Small model running
2	data_size < threshold	False	Check resource_limit	Preparing to scale
3	resource_limit	True	scale_vertically()	Resources increased on single node
4	resource_limit	False	scale_horizontally()	Added more nodes to cluster
5	Monitor & Optimize	N/A	Adjust strategy if needed	System optimized for load

💡 Scaling strategy chosen based on data size and resource availability

Status Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	Final
data_size	small	small	small	small	small
resource_limit	N/A	N/A	True or False	N/A	N/A
model_state	idle	small model running	preparing to scale	scaled vertically or horizontally	optimized

Key Moments - 3 Insights

Why can't we use the same scaling strategy for all situations?

Because resource limits and data size vary, the execution_table shows different paths: vertical scaling when resources are limited, horizontal when adding nodes is possible.

What happens if data size is small?

The execution_table row 1 shows the system uses a small model without scaling, saving resources.

Why monitor after scaling?

Step 5 in execution_table shows monitoring to optimize performance and adjust strategy if needed, ensuring efficient scaling.

Visual Quiz - 3 Questions

Look at the execution_table, what action is taken when data_size < threshold is True?

A use_small_model()

B scale_vertically()

C scale_horizontally()

D Monitor & Optimize

💡 Hint

Check Step 1 in execution_table under Action Taken

At which step does the system decide to add more nodes?

A Step 2

B Step 3

C Step 4

D Step 5

💡 Hint

Look at Step 4 in execution_table where scale_horizontally() is called

If resource_limit is True, which scaling strategy is chosen?

A Horizontal scaling

B Vertical scaling

C Hybrid scaling

D No scaling

💡 Hint

Refer to Step 3 in execution_table where scale_vertically() is executed

Concept Snapshot

Scaling depends on data size and resources.
Small data uses small models.
Vertical scaling adds resources to one node.
Horizontal scaling adds more nodes.
Monitor after scaling to optimize.
Choose strategy based on limits.

Full Transcript

This visual execution shows how scaling strategies differ based on data size and resource limits. Starting with a small model, the system checks if scaling is needed. If data size is small, it continues without scaling. If scaling is needed, it chooses vertical scaling if resources are limited, or horizontal scaling if adding nodes is possible. After scaling, monitoring ensures performance optimization. This helps understand why one size does not fit all in scaling strategies.